Merge branch 'master' into model-no-duplicate-code-upload

Author: laurenyu

CHANGELOG.rst

@@ -2,8 +2,11 @@
 CHANGELOG
 =========
 
-1.4.3dev
-========
+1.5.0
+=====
+* feature: Add Support for PyTorch Framework
+* feature: Estimators: add support for TensorFlow 1.7.0
+* feature: Estimators: add support for TensorFlow 1.8.0
 * feature: Allow Local Serving of Models in S3
 * enhancement: Allow option for ``HyperparameterTuner`` to not include estimator metadata in job
 * bug-fix: Estimators: Join tensorboard thread after fitting

README.rst

@@ -49,7 +49,7 @@ You can install from source by cloning this repository and issuing a pip install
 
     git clone https://github.com/aws/sagemaker-python-sdk.git
     python setup.py sdist
-    pip install dist/sagemaker-1.4.2.tar.gz
+    pip install dist/sagemaker-1.5.0.tar.gz
 
 Supported Python versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -215,7 +215,7 @@ TensorFlow SageMaker Estimators allow you to run your own TensorFlow
 training algorithms on SageMaker Learner, and to host your own TensorFlow
 models on SageMaker Hosting.
 
-Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``.
+Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``.
 
 More details at `TensorFlow SageMaker Estimators and Models`_.
 
@@ -236,6 +236,20 @@ More details at `Chainer SageMaker Estimators and Models`_.
 .. _Chainer SageMaker Estimators and Models: src/sagemaker/chainer/README.rst
 
 
+PyTorch SageMaker Estimators
+-------------------------------
+
+With PyTorch Estimators, you can train and host PyTorch models on Amazon SageMaker.
+
+Supported versions of PyTorch: ``0.4.0``
+
+You can visit the PyTorch repository at https://github.com/pytorch/pytorch.
+
+More details at `PyTorch SageMaker Estimators and Models`_.
+
+.. _PyTorch SageMaker Estimators and Models: src/sagemaker/pytorch/README.rst
+
+
 AWS SageMaker Estimators
 ------------------------
 Amazon SageMaker provides several built-in machine learning algorithms that you can use for a variety of problem types.

setup.py

@@ -23,7 +23,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.4.2",
+      version="1.5.0",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

src/sagemaker/fw_utils.py

@@ -156,7 +156,7 @@ def framework_name_from_image(image_name):
     else:
         # extract framework, python version and image tag
         # We must support both the legacy and current image name format.
-        name_pattern = re.compile('^sagemaker-(tensorflow|mxnet|chainer):(.*?)-(.*?)-(py2|py3)$')
+        name_pattern = re.compile('^sagemaker-(tensorflow|mxnet|chainer|pytorch):(.*?)-(.*?)-(py2|py3)$')
         legacy_name_pattern = re.compile('^sagemaker-(tensorflow|mxnet)-(py2|py3)-(cpu|gpu):(.*)$')
         name_match = name_pattern.match(sagemaker_match.group(8))
         legacy_match = legacy_name_pattern.match(sagemaker_match.group(8))